Distinguishing Felsenstein zone from Farris zone using neural networks

Maximum likelihood and maximum parsimony are two key methods for phylogenetic tree reconstruction. Under certain conditions, each of these two methods can perform more or less efficiently than the other. We show that a neural network can efficiently distinguish between four-taxon alignments that were evolved under conditions conducive to long-branch attraction, or long-branch repulsion. The feedback from the neural network can be used to select the most efficient tree reconstruction method yielding increased accuracy, when compared to a rigid choice of reconstruction methods. When applied to the contentious case of Strepsiptera evolution, our method agrees with the current scientific view.